Saliency of Interest Points under Scale Changes

Interest point detectors are commonly employed to reduce the amount of data to be processed. The ideal interest point detector would robustly select those features which are most appropriate or salient for the application and data at hand. There is however a tradeoff between the robustness and the discriminance of the selected features. Whereas robustness in terms of repeatability is relatively well explored, the discriminance of interest points is rarely discussed. This paper formalizes the notion of saliency and evaluates three state-of-the-art interest point detectors with respect to their capability of selecting salient image features in two recognition settings.